{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PVTAnalysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPdwROIif2x2AM/KDQl7S5N",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/PVTAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZTWhjLzGT1P",
        "colab_type": "text"
      },
      "source": [
        "#Real Gas - PVT properties calculation\n",
        "\n",
        "Divyanshu vyas | dvyas13ad@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ERaug9gGr8L",
        "colab_type": "text"
      },
      "source": [
        "###Problem Statement- \n",
        "\n",
        "A Gas Reservoir has P(initial) = 3000 psia and T = 180 deg F, and the given composition. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofyjBC1GHm9_",
        "colab_type": "text"
      },
      "source": [
        "Carry out the PVT calculation, starting from Z calculation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Gts2ME8eYH_",
        "colab_type": "text"
      },
      "source": [
        "(Courtsey: Standing, Katz, Dranchuk et al.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pAQTOC-GObZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "%matplotlib inline"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dihvVsvoGmpI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "1a871b67-afc4-4b5f-c448-2a8e179335f2"
      },
      "source": [
        "#Composition\n",
        "comp = {'element':['CO2','N2','C1','C2','C3','i-C4','n-C4'],\n",
        "        'yi':[0.02,0.01,0.85,0.04,0.03,0.03,0.02],\n",
        "        'Tci(R)':[547.91,227.49,343.33,549.92,666.06,734.46,765.62],\n",
        "        'Pci(psi)':[1071,493.1,666.4,706.5,616.4,527.9,550.6]}\n",
        "\n",
        "df = pd.DataFrame(comp)\n",
        "\n",
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>element</th>\n",
              "      <th>yi</th>\n",
              "      <th>Tci(R)</th>\n",
              "      <th>Pci(psi)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CO2</td>\n",
              "      <td>0.02</td>\n",
              "      <td>547.91</td>\n",
              "      <td>1071.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>N2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>227.49</td>\n",
              "      <td>493.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C1</td>\n",
              "      <td>0.85</td>\n",
              "      <td>343.33</td>\n",
              "      <td>666.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C2</td>\n",
              "      <td>0.04</td>\n",
              "      <td>549.92</td>\n",
              "      <td>706.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C3</td>\n",
              "      <td>0.03</td>\n",
              "      <td>666.06</td>\n",
              "      <td>616.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>i-C4</td>\n",
              "      <td>0.03</td>\n",
              "      <td>734.46</td>\n",
              "      <td>527.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>n-C4</td>\n",
              "      <td>0.02</td>\n",
              "      <td>765.62</td>\n",
              "      <td>550.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  element    yi  Tci(R)  Pci(psi)\n",
              "0     CO2  0.02  547.91    1071.0\n",
              "1      N2  0.01  227.49     493.1\n",
              "2      C1  0.85  343.33     666.4\n",
              "3      C2  0.04  549.92     706.5\n",
              "4      C3  0.03  666.06     616.4\n",
              "5    i-C4  0.03  734.46     527.9\n",
              "6    n-C4  0.02  765.62     550.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-CzMcjyHgyr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['yi*Tci'] = df['yi']*df['Tci(R)']\n",
        "\n",
        "df['yi*Pci'] = df['yi']*df['Pci(psi)']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBY2wo1bKT5f",
        "colab_type": "text"
      },
      "source": [
        "> Tpc = Summation (yi*Tci)\n",
        "\n",
        "> Ppc = Summation (yi*Pci)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4bxfzGhNQqy",
        "colab_type": "text"
      },
      "source": [
        "Approach 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZAQ1YVbJRz_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5b789827-9d20-4dfd-8903-108e10b73379"
      },
      "source": [
        "#Pseudo Critical Temperature\n",
        "Tpc = df['yi*Tci'].sum()\n",
        "\n",
        "#Pseudo Critical Pressure\n",
        "Ppc = df['yi*Pci'].sum()\n",
        "\n",
        "print(Tpc,Ppc)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "384.3884 666.3919999999998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d25zJfRhJaFu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Reservoir Properties\n",
        "P = 3000  #Reservoir Pressure\n",
        "\n",
        "T = 180 + 460 #deg Rankine"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jsUdXiZK02S",
        "colab_type": "text"
      },
      "source": [
        "> Ppr = P/Ppc\n",
        "\n",
        "> Tpr = T/Tpc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwMTdyPKKxNR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Pseudo Reduced Properties\n",
        "Ppr = P/Ppc\n",
        "\n",
        "Tpr = T/Tpc"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCzOPIutLRGn",
        "colab_type": "text"
      },
      "source": [
        "#Approach 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPPWzk3VLBgx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "f67c909e-60bf-4351-896e-7957b10b3e60"
      },
      "source": [
        "gas = df[['element','yi']]\n",
        "\n",
        "gas['Mi'] = np.array([44,28,16,30,44,58,58])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCAgEQU7LeOr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "656c8c1a-a67a-4f86-9842-ecdbfe5e02b4"
      },
      "source": [
        "gas['yi*Mi'] = gas['yi']*gas['Mi']\n",
        "\n",
        "Mav = gas['yi*Mi'].sum()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-__IlVILfIP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7da56b5f-ce7b-428e-d8c8-2383aec44f43"
      },
      "source": [
        "Mav"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.179999999999996"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8NqwyG-MIfC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vg = Mav/28.97"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yavci4i7MS_u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "321f8194-9384-48ff-88da-12b3554de856"
      },
      "source": [
        "vg"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6965826717293752"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iAteDrjMUBG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b4473910-e819-4505-c5b7-bc1a4abd8c0e"
      },
      "source": [
        "T_pc = 168 + 325*vg -12.5*(vg**2)\n",
        "\n",
        "# T_pc\n",
        "\n",
        "P_pc = 677 + 15*vg - 37.5*(vg**2)\n",
        "\n",
        "print(T_pc,P_pc)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "388.3240255801265 669.2527118801793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r112cUv1ZlzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "T_pr = T/T_pc\n",
        "\n",
        "P_pr = P/P_pc"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2ce_RyBMhjZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Standing and KatZ data-set. \n",
        "\n",
        "# 1.05->\n",
        "# 1 0.204 0.937 TRUE       0.2   4.00e- 3\n",
        "#  2 0.3   0.905 TRUE       0.3  -5.55e-17\n",
        "#  3 0.405 0.866 TRUE       0.4   5.00e- 3\n",
        "#  4 0.504 0.829 TRUE       0.5   4.00e- 3\n",
        "#  5 0.602 0.79  TRUE       0.6   2.00e- 3\n",
        "#  6 0.7   0.748 TRUE       0.7  -1.11e-16\n",
        "#  7 1     0.589 TRUE       1     0.      \n",
        "#  8 1.20  0.44  TRUE       1.2   3.00e- 3\n",
        "#  9 1.30  0.35  TRUE       1.3  10.00e- 4\n",
        "# 10 1.33  0.319 FALSE      1.33  0.      \n",
        "# ppr_1 = [0.204,0.3,0.405,0.504,0.602,0.7,1,1.20,1.30,1.33]\n",
        "# z_1 = [0.937,0.905,0.866,0.829,0.79,0.748,0.89,0.44,0.3,0.319]\n",
        "\n",
        "# plt.plot(ppr_1,z_1)\n",
        "# 1.3->\n",
        "#      Ppr     z isNear Ppr_near      diff\n",
        "#    <dbl> <dbl> <lgl>     <dbl>     <dbl>\n",
        "#  1 0.201 0.967 TRUE        0.2  1.00e- 3\n",
        "#  2 0.302 0.95  TRUE        0.3  2.00e- 3\n",
        "#  3 0.401 0.935 TRUE        0.4  1.00e- 3\n",
        "#  4 0.504 0.916 TRUE        0.5  4.00e- 3\n",
        "#  5 0.6   0.901 TRUE        0.6 -1.11e-16\n",
        "#  6 0.804 0.868 TRUE        0.8  4.00e- 3\n",
        "#  7 1.00  0.835 TRUE        1    2.00e- 3\n",
        "#  8 1.20  0.804 TRUE        1.2  2.00e- 3\n",
        "#  9 1.30  0.788 TRUE        1.3  3.00e- 3\n",
        "# 10 1.50  0.756 TRUE        1.5  3.00e- 3\n",
        "\n",
        "\n",
        "# 1.5->\n",
        "#      Ppr     z isNear Ppr_near     diff\n",
        "#    <dbl> <dbl> <lgl>     <dbl>    <dbl>\n",
        "#  1 0.202 0.978 TRUE        0.2 0.002   \n",
        "#  2 0.303 0.967 TRUE        0.3 0.00300 \n",
        "#  3 0.404 0.958 TRUE        0.4 0.004   \n",
        "#  4 0.506 0.948 TRUE        0.5 0.006   \n",
        "#  5 0.701 0.929 TRUE        0.7 0.001000\n",
        "#  6 0.801 0.919 TRUE        0.8 0.001   \n",
        "#  7 1     0.9   TRUE        1   0       \n",
        "#  8 1.30  0.875 TRUE        1.3 0.004   \n",
        "#  9 1.50  0.859 TRUE        1.5 0.00500 \n",
        "# 10 1.80  0.836 TRUE        1.8 0.00300 \n",
        "# # … with 27 more rows"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AreXslhfYPau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def z_calculator(T_pr,P_pr):\n",
        "  A = 1.39 * ((T_pr - 0.92) ** 0.5) - (0.36*T_pr) - 0.10\n",
        "  E = 9*(T_pr - 1)\n",
        "  B = (0.62 - 0.23*T_pr)*P_pr + ((0.066 / (T_pr - 0.86)) - 0.037)*(P_pr**2) + (0.32*(P_pr**2)) / (10*E)\n",
        "  C = 0.132 - 0.32 * math.log(T_pr)\n",
        "  F = 0.3106 - 0.49*T_pr + 0.1824*(T_pr**2)\n",
        "  D = 10 ** F\n",
        "\n",
        "  z = A + ((1 - A) / math.exp(B)) + (C * (P_pr ** D))\n",
        "\n",
        "  return z\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-d0ZDNwCaYAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = z_calculator(T_pr,P_pr)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D20WpelJadIp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = np.linspace(1000,5000,50)\n",
        "\n",
        "ppr = p/P_pc\n",
        "\n",
        "tpr = [1.05,1.1,1.15,1.2,1.25,1.3]\n",
        "\n",
        "\n",
        "z_o = []\n",
        "\n",
        "for t in tpr:\n",
        "  z_in = []\n",
        "  for pr in ppr:\n",
        "\n",
        "    res = z_calculator(t,pr)\n",
        "\n",
        "    z_in.append(res)\n",
        "  \n",
        "  z_o.append(z_in)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaLXLue2aeB2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z_df = pd.DataFrame(z_o)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaHNhC0sb8jO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z_df = z_df.T"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZfLz7N-cFnD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2e7634f5-e62c-4fba-e3e1-e844e2de4be9"
      },
      "source": [
        "z_df.columns = ['T_pr(1.05)','T_pr(1.10)','T_pr(1.15)','T_pr(1.20)','T_pr(1.25)','T_pr(1.30)']\n",
        "\n",
        "z_df.index = p/P_pc\n",
        "z_df"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>T_pr(1.05)</th>\n",
              "      <th>T_pr(1.10)</th>\n",
              "      <th>T_pr(1.15)</th>\n",
              "      <th>T_pr(1.20)</th>\n",
              "      <th>T_pr(1.25)</th>\n",
              "      <th>T_pr(1.30)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.494204</th>\n",
              "      <td>0.433389</td>\n",
              "      <td>0.528676</td>\n",
              "      <td>0.590395</td>\n",
              "      <td>0.634488</td>\n",
              "      <td>0.667388</td>\n",
              "      <td>0.692586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.616180</th>\n",
              "      <td>0.406309</td>\n",
              "      <td>0.501493</td>\n",
              "      <td>0.564451</td>\n",
              "      <td>0.609952</td>\n",
              "      <td>0.644160</td>\n",
              "      <td>0.670495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.738156</th>\n",
              "      <td>0.384548</td>\n",
              "      <td>0.477999</td>\n",
              "      <td>0.541198</td>\n",
              "      <td>0.587445</td>\n",
              "      <td>0.622498</td>\n",
              "      <td>0.649637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.860131</th>\n",
              "      <td>0.367867</td>\n",
              "      <td>0.458193</td>\n",
              "      <td>0.520704</td>\n",
              "      <td>0.567052</td>\n",
              "      <td>0.602490</td>\n",
              "      <td>0.630092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.982107</th>\n",
              "      <td>0.355914</td>\n",
              "      <td>0.441991</td>\n",
              "      <td>0.502980</td>\n",
              "      <td>0.548820</td>\n",
              "      <td>0.584192</td>\n",
              "      <td>0.611920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.104083</th>\n",
              "      <td>0.348263</td>\n",
              "      <td>0.429240</td>\n",
              "      <td>0.487983</td>\n",
              "      <td>0.532756</td>\n",
              "      <td>0.567635</td>\n",
              "      <td>0.595158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.226059</th>\n",
              "      <td>0.344447</td>\n",
              "      <td>0.419735</td>\n",
              "      <td>0.475630</td>\n",
              "      <td>0.518838</td>\n",
              "      <td>0.552825</td>\n",
              "      <td>0.579828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.348035</th>\n",
              "      <td>0.343983</td>\n",
              "      <td>0.413234</td>\n",
              "      <td>0.465802</td>\n",
              "      <td>0.507010</td>\n",
              "      <td>0.539745</td>\n",
              "      <td>0.565933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.470010</th>\n",
              "      <td>0.346394</td>\n",
              "      <td>0.409470</td>\n",
              "      <td>0.458352</td>\n",
              "      <td>0.497198</td>\n",
              "      <td>0.528359</td>\n",
              "      <td>0.553459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.591986</th>\n",
              "      <td>0.351230</td>\n",
              "      <td>0.408165</td>\n",
              "      <td>0.453114</td>\n",
              "      <td>0.489304</td>\n",
              "      <td>0.518613</td>\n",
              "      <td>0.542382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.713962</th>\n",
              "      <td>0.358076</td>\n",
              "      <td>0.409038</td>\n",
              "      <td>0.449911</td>\n",
              "      <td>0.483218</td>\n",
              "      <td>0.510441</td>\n",
              "      <td>0.532662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.835938</th>\n",
              "      <td>0.366563</td>\n",
              "      <td>0.411817</td>\n",
              "      <td>0.448557</td>\n",
              "      <td>0.478818</td>\n",
              "      <td>0.503765</td>\n",
              "      <td>0.524251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.957914</th>\n",
              "      <td>0.376366</td>\n",
              "      <td>0.416242</td>\n",
              "      <td>0.448865</td>\n",
              "      <td>0.475977</td>\n",
              "      <td>0.498499</td>\n",
              "      <td>0.517092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.079890</th>\n",
              "      <td>0.387211</td>\n",
              "      <td>0.422071</td>\n",
              "      <td>0.450654</td>\n",
              "      <td>0.474562</td>\n",
              "      <td>0.494551</td>\n",
              "      <td>0.511123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.201865</th>\n",
              "      <td>0.398868</td>\n",
              "      <td>0.429085</td>\n",
              "      <td>0.453747</td>\n",
              "      <td>0.474442</td>\n",
              "      <td>0.491824</td>\n",
              "      <td>0.506276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.323841</th>\n",
              "      <td>0.411148</td>\n",
              "      <td>0.437087</td>\n",
              "      <td>0.457979</td>\n",
              "      <td>0.475488</td>\n",
              "      <td>0.490223</td>\n",
              "      <td>0.502478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.445817</th>\n",
              "      <td>0.423899</td>\n",
              "      <td>0.445902</td>\n",
              "      <td>0.463195</td>\n",
              "      <td>0.477574</td>\n",
              "      <td>0.489650</td>\n",
              "      <td>0.499658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.567793</th>\n",
              "      <td>0.437002</td>\n",
              "      <td>0.455382</td>\n",
              "      <td>0.469254</td>\n",
              "      <td>0.480582</td>\n",
              "      <td>0.490009</td>\n",
              "      <td>0.497741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.689769</th>\n",
              "      <td>0.450362</td>\n",
              "      <td>0.465397</td>\n",
              "      <td>0.476030</td>\n",
              "      <td>0.484401</td>\n",
              "      <td>0.491210</td>\n",
              "      <td>0.496654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.811745</th>\n",
              "      <td>0.463910</td>\n",
              "      <td>0.475838</td>\n",
              "      <td>0.483408</td>\n",
              "      <td>0.488926</td>\n",
              "      <td>0.493163</td>\n",
              "      <td>0.496325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.933720</th>\n",
              "      <td>0.477590</td>\n",
              "      <td>0.486616</td>\n",
              "      <td>0.491288</td>\n",
              "      <td>0.494062</td>\n",
              "      <td>0.495785</td>\n",
              "      <td>0.496685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.055696</th>\n",
              "      <td>0.491365</td>\n",
              "      <td>0.497656</td>\n",
              "      <td>0.499584</td>\n",
              "      <td>0.499724</td>\n",
              "      <td>0.499000</td>\n",
              "      <td>0.497667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.177672</th>\n",
              "      <td>0.505203</td>\n",
              "      <td>0.508899</td>\n",
              "      <td>0.508221</td>\n",
              "      <td>0.505834</td>\n",
              "      <td>0.502734</td>\n",
              "      <td>0.499206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.299648</th>\n",
              "      <td>0.519086</td>\n",
              "      <td>0.520296</td>\n",
              "      <td>0.517133</td>\n",
              "      <td>0.512324</td>\n",
              "      <td>0.506921</td>\n",
              "      <td>0.501245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.421624</th>\n",
              "      <td>0.532998</td>\n",
              "      <td>0.531809</td>\n",
              "      <td>0.526269</td>\n",
              "      <td>0.519134</td>\n",
              "      <td>0.511502</td>\n",
              "      <td>0.503726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.543600</th>\n",
              "      <td>0.546928</td>\n",
              "      <td>0.543409</td>\n",
              "      <td>0.535581</td>\n",
              "      <td>0.526209</td>\n",
              "      <td>0.516422</td>\n",
              "      <td>0.506598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.665575</th>\n",
              "      <td>0.560870</td>\n",
              "      <td>0.555071</td>\n",
              "      <td>0.545033</td>\n",
              "      <td>0.533505</td>\n",
              "      <td>0.521633</td>\n",
              "      <td>0.509815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.787551</th>\n",
              "      <td>0.574819</td>\n",
              "      <td>0.566779</td>\n",
              "      <td>0.554595</td>\n",
              "      <td>0.540982</td>\n",
              "      <td>0.527091</td>\n",
              "      <td>0.513332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.909527</th>\n",
              "      <td>0.588771</td>\n",
              "      <td>0.578518</td>\n",
              "      <td>0.564241</td>\n",
              "      <td>0.548607</td>\n",
              "      <td>0.532758</td>\n",
              "      <td>0.517111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.031503</th>\n",
              "      <td>0.602726</td>\n",
              "      <td>0.590279</td>\n",
              "      <td>0.573952</td>\n",
              "      <td>0.556352</td>\n",
              "      <td>0.538601</td>\n",
              "      <td>0.521115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.153479</th>\n",
              "      <td>0.616680</td>\n",
              "      <td>0.602055</td>\n",
              "      <td>0.583711</td>\n",
              "      <td>0.564192</td>\n",
              "      <td>0.544592</td>\n",
              "      <td>0.525315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.275455</th>\n",
              "      <td>0.630633</td>\n",
              "      <td>0.613839</td>\n",
              "      <td>0.593506</td>\n",
              "      <td>0.572109</td>\n",
              "      <td>0.550704</td>\n",
              "      <td>0.529682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.397430</th>\n",
              "      <td>0.644586</td>\n",
              "      <td>0.625628</td>\n",
              "      <td>0.603327</td>\n",
              "      <td>0.580085</td>\n",
              "      <td>0.556917</td>\n",
              "      <td>0.534190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.519406</th>\n",
              "      <td>0.658537</td>\n",
              "      <td>0.637420</td>\n",
              "      <td>0.613166</td>\n",
              "      <td>0.588108</td>\n",
              "      <td>0.563212</td>\n",
              "      <td>0.538819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.641382</th>\n",
              "      <td>0.672486</td>\n",
              "      <td>0.649211</td>\n",
              "      <td>0.623017</td>\n",
              "      <td>0.596167</td>\n",
              "      <td>0.569575</td>\n",
              "      <td>0.543548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.763358</th>\n",
              "      <td>0.686434</td>\n",
              "      <td>0.661002</td>\n",
              "      <td>0.632877</td>\n",
              "      <td>0.604253</td>\n",
              "      <td>0.575990</td>\n",
              "      <td>0.548363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.885334</th>\n",
              "      <td>0.700380</td>\n",
              "      <td>0.672790</td>\n",
              "      <td>0.642741</td>\n",
              "      <td>0.612358</td>\n",
              "      <td>0.582449</td>\n",
              "      <td>0.553248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.007309</th>\n",
              "      <td>0.714324</td>\n",
              "      <td>0.684575</td>\n",
              "      <td>0.652607</td>\n",
              "      <td>0.620478</td>\n",
              "      <td>0.588942</td>\n",
              "      <td>0.558192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.129285</th>\n",
              "      <td>0.728266</td>\n",
              "      <td>0.696357</td>\n",
              "      <td>0.662473</td>\n",
              "      <td>0.628608</td>\n",
              "      <td>0.595461</td>\n",
              "      <td>0.563183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.251261</th>\n",
              "      <td>0.742207</td>\n",
              "      <td>0.708136</td>\n",
              "      <td>0.672337</td>\n",
              "      <td>0.636744</td>\n",
              "      <td>0.602000</td>\n",
              "      <td>0.568214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.373237</th>\n",
              "      <td>0.756146</td>\n",
              "      <td>0.719911</td>\n",
              "      <td>0.682199</td>\n",
              "      <td>0.644883</td>\n",
              "      <td>0.608554</td>\n",
              "      <td>0.573276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.495213</th>\n",
              "      <td>0.770083</td>\n",
              "      <td>0.731682</td>\n",
              "      <td>0.692059</td>\n",
              "      <td>0.653025</td>\n",
              "      <td>0.615120</td>\n",
              "      <td>0.578363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.617189</th>\n",
              "      <td>0.784018</td>\n",
              "      <td>0.743450</td>\n",
              "      <td>0.701914</td>\n",
              "      <td>0.661166</td>\n",
              "      <td>0.621693</td>\n",
              "      <td>0.583470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.739164</th>\n",
              "      <td>0.797952</td>\n",
              "      <td>0.755214</td>\n",
              "      <td>0.711766</td>\n",
              "      <td>0.669307</td>\n",
              "      <td>0.628272</td>\n",
              "      <td>0.588593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.861140</th>\n",
              "      <td>0.811884</td>\n",
              "      <td>0.766974</td>\n",
              "      <td>0.721614</td>\n",
              "      <td>0.677445</td>\n",
              "      <td>0.634854</td>\n",
              "      <td>0.593728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.983116</th>\n",
              "      <td>0.825814</td>\n",
              "      <td>0.778731</td>\n",
              "      <td>0.731458</td>\n",
              "      <td>0.685581</td>\n",
              "      <td>0.641437</td>\n",
              "      <td>0.598872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.105092</th>\n",
              "      <td>0.839743</td>\n",
              "      <td>0.790484</td>\n",
              "      <td>0.741297</td>\n",
              "      <td>0.693713</td>\n",
              "      <td>0.648021</td>\n",
              "      <td>0.604022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.227068</th>\n",
              "      <td>0.853671</td>\n",
              "      <td>0.802234</td>\n",
              "      <td>0.751133</td>\n",
              "      <td>0.701842</td>\n",
              "      <td>0.654604</td>\n",
              "      <td>0.609177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.349044</th>\n",
              "      <td>0.867597</td>\n",
              "      <td>0.813980</td>\n",
              "      <td>0.760964</td>\n",
              "      <td>0.709967</td>\n",
              "      <td>0.661186</td>\n",
              "      <td>0.614335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.471019</th>\n",
              "      <td>0.881521</td>\n",
              "      <td>0.825723</td>\n",
              "      <td>0.770791</td>\n",
              "      <td>0.718089</td>\n",
              "      <td>0.667765</td>\n",
              "      <td>0.619495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          T_pr(1.05)  T_pr(1.10)  ...  T_pr(1.25)  T_pr(1.30)\n",
              "1.494204    0.433389    0.528676  ...    0.667388    0.692586\n",
              "1.616180    0.406309    0.501493  ...    0.644160    0.670495\n",
              "1.738156    0.384548    0.477999  ...    0.622498    0.649637\n",
              "1.860131    0.367867    0.458193  ...    0.602490    0.630092\n",
              "1.982107    0.355914    0.441991  ...    0.584192    0.611920\n",
              "2.104083    0.348263    0.429240  ...    0.567635    0.595158\n",
              "2.226059    0.344447    0.419735  ...    0.552825    0.579828\n",
              "2.348035    0.343983    0.413234  ...    0.539745    0.565933\n",
              "2.470010    0.346394    0.409470  ...    0.528359    0.553459\n",
              "2.591986    0.351230    0.408165  ...    0.518613    0.542382\n",
              "2.713962    0.358076    0.409038  ...    0.510441    0.532662\n",
              "2.835938    0.366563    0.411817  ...    0.503765    0.524251\n",
              "2.957914    0.376366    0.416242  ...    0.498499    0.517092\n",
              "3.079890    0.387211    0.422071  ...    0.494551    0.511123\n",
              "3.201865    0.398868    0.429085  ...    0.491824    0.506276\n",
              "3.323841    0.411148    0.437087  ...    0.490223    0.502478\n",
              "3.445817    0.423899    0.445902  ...    0.489650    0.499658\n",
              "3.567793    0.437002    0.455382  ...    0.490009    0.497741\n",
              "3.689769    0.450362    0.465397  ...    0.491210    0.496654\n",
              "3.811745    0.463910    0.475838  ...    0.493163    0.496325\n",
              "3.933720    0.477590    0.486616  ...    0.495785    0.496685\n",
              "4.055696    0.491365    0.497656  ...    0.499000    0.497667\n",
              "4.177672    0.505203    0.508899  ...    0.502734    0.499206\n",
              "4.299648    0.519086    0.520296  ...    0.506921    0.501245\n",
              "4.421624    0.532998    0.531809  ...    0.511502    0.503726\n",
              "4.543600    0.546928    0.543409  ...    0.516422    0.506598\n",
              "4.665575    0.560870    0.555071  ...    0.521633    0.509815\n",
              "4.787551    0.574819    0.566779  ...    0.527091    0.513332\n",
              "4.909527    0.588771    0.578518  ...    0.532758    0.517111\n",
              "5.031503    0.602726    0.590279  ...    0.538601    0.521115\n",
              "5.153479    0.616680    0.602055  ...    0.544592    0.525315\n",
              "5.275455    0.630633    0.613839  ...    0.550704    0.529682\n",
              "5.397430    0.644586    0.625628  ...    0.556917    0.534190\n",
              "5.519406    0.658537    0.637420  ...    0.563212    0.538819\n",
              "5.641382    0.672486    0.649211  ...    0.569575    0.543548\n",
              "5.763358    0.686434    0.661002  ...    0.575990    0.548363\n",
              "5.885334    0.700380    0.672790  ...    0.582449    0.553248\n",
              "6.007309    0.714324    0.684575  ...    0.588942    0.558192\n",
              "6.129285    0.728266    0.696357  ...    0.595461    0.563183\n",
              "6.251261    0.742207    0.708136  ...    0.602000    0.568214\n",
              "6.373237    0.756146    0.719911  ...    0.608554    0.573276\n",
              "6.495213    0.770083    0.731682  ...    0.615120    0.578363\n",
              "6.617189    0.784018    0.743450  ...    0.621693    0.583470\n",
              "6.739164    0.797952    0.755214  ...    0.628272    0.588593\n",
              "6.861140    0.811884    0.766974  ...    0.634854    0.593728\n",
              "6.983116    0.825814    0.778731  ...    0.641437    0.598872\n",
              "7.105092    0.839743    0.790484  ...    0.648021    0.604022\n",
              "7.227068    0.853671    0.802234  ...    0.654604    0.609177\n",
              "7.349044    0.867597    0.813980  ...    0.661186    0.614335\n",
              "7.471019    0.881521    0.825723  ...    0.667765    0.619495\n",
              "\n",
              "[50 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwkd71VMcU9G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "e2d95639-c213-478e-c4eb-fab7a36a5fb4"
      },
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.style.use('default')\n",
        "\n",
        "\n",
        "z_df.plot()\n",
        "\n",
        "\n",
        "plt.title('Z(Compressibility Factor) Calculation')\n",
        "\n",
        "plt.xlabel('Pseudo-Reduced Pressure')\n",
        "\n",
        "plt.ylabel('Z')\n",
        "\n",
        "\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOFLOQfWdKGr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}